Overview

Dataset statistics

Number of variables44
Number of observations115037
Missing cells171246
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.6 MiB
Average record size in memory352.0 B

Variable types

Categorical25
Numeric19

Alerts

order_id has a high cardinality: 96036 distinct values High cardinality
customer_id has a high cardinality: 96036 distinct values High cardinality
order_purchase_timestamp has a high cardinality: 95515 distinct values High cardinality
order_approved_at has a high cardinality: 87916 distinct values High cardinality
order_delivered_carrier_date has a high cardinality: 78867 distinct values High cardinality
order_delivered_customer_date has a high cardinality: 93239 distinct values High cardinality
order_estimated_delivery_date has a high cardinality: 449 distinct values High cardinality
product_id has a high cardinality: 32063 distinct values High cardinality
seller_id has a high cardinality: 3021 distinct values High cardinality
shipping_limit_date has a high cardinality: 90958 distinct values High cardinality
seller_city has a high cardinality: 604 distinct values High cardinality
customer_unique_id has a high cardinality: 92939 distinct values High cardinality
customer_city has a high cardinality: 4044 distinct values High cardinality
product_category_name has a high cardinality: 71 distinct values High cardinality
product_category_name_english has a high cardinality: 71 distinct values High cardinality
review_id has a high cardinality: 95840 distinct values High cardinality
review_comment_title has a high cardinality: 4455 distinct values High cardinality
review_comment_message has a high cardinality: 35020 distinct values High cardinality
review_creation_date has a high cardinality: 632 distinct values High cardinality
review_answer_timestamp has a high cardinality: 95685 distinct values High cardinality
price is highly correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly correlated with price and 3 other fieldsHigh correlation
product_length_cm is highly correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly correlated with product_weight_gHigh correlation
product_width_cm is highly correlated with product_weight_g and 1 other fieldsHigh correlation
payment_value is highly correlated with priceHigh correlation
price is highly correlated with payment_valueHigh correlation
freight_value is highly correlated with product_weight_gHigh correlation
product_weight_g is highly correlated with freight_value and 2 other fieldsHigh correlation
product_length_cm is highly correlated with product_width_cmHigh correlation
product_height_cm is highly correlated with product_weight_gHigh correlation
product_width_cm is highly correlated with product_weight_g and 1 other fieldsHigh correlation
payment_value is highly correlated with priceHigh correlation
price is highly correlated with payment_valueHigh correlation
payment_value is highly correlated with priceHigh correlation
product_category_name is highly correlated with product_category_name_englishHigh correlation
product_category_name_english is highly correlated with product_category_nameHigh correlation
price is highly correlated with payment_valueHigh correlation
seller_zip_code_prefix is highly correlated with seller_state and 4 other fieldsHigh correlation
seller_state is highly correlated with seller_zip_code_prefix and 4 other fieldsHigh correlation
customer_zip_code_prefix is highly correlated with customer_state and 2 other fieldsHigh correlation
customer_state is highly correlated with customer_zip_code_prefix and 2 other fieldsHigh correlation
product_category_name is highly correlated with seller_zip_code_prefix and 9 other fieldsHigh correlation
product_description_lenght is highly correlated with product_category_name and 1 other fieldsHigh correlation
product_weight_g is highly correlated with product_category_name and 1 other fieldsHigh correlation
product_length_cm is highly correlated with product_category_name and 2 other fieldsHigh correlation
product_height_cm is highly correlated with product_category_name and 1 other fieldsHigh correlation
product_width_cm is highly correlated with product_category_name and 2 other fieldsHigh correlation
payment_value is highly correlated with priceHigh correlation
product_category_name_english is highly correlated with seller_zip_code_prefix and 9 other fieldsHigh correlation
geolocation_lat_customer is highly correlated with customer_zip_code_prefix and 2 other fieldsHigh correlation
geolocation_lng_customer is highly correlated with customer_zip_code_prefix and 2 other fieldsHigh correlation
geolocation_lat_seller is highly correlated with seller_zip_code_prefix and 4 other fieldsHigh correlation
geolocation_lng_seller is highly correlated with seller_zip_code_prefix and 4 other fieldsHigh correlation
order_delivered_carrier_date has 1185 (1.0%) missing values Missing
order_delivered_customer_date has 2384 (2.1%) missing values Missing
review_comment_title has 101302 (88.1%) missing values Missing
review_comment_message has 66357 (57.7%) missing values Missing
order_id is uniformly distributed Uniform
customer_id is uniformly distributed Uniform
order_purchase_timestamp is uniformly distributed Uniform
order_approved_at is uniformly distributed Uniform
order_delivered_customer_date is uniformly distributed Uniform
shipping_limit_date is uniformly distributed Uniform
customer_unique_id is uniformly distributed Uniform
review_id is uniformly distributed Uniform
review_answer_timestamp is uniformly distributed Uniform

Reproduction

Analysis started2022-04-06 15:19:26.999147
Analysis finished2022-04-06 15:21:52.952512
Duration2 minutes and 25.95 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

order_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct96036
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
895ab968e7bb0d5659d16cd74cd1650c
 
63
fedcd9f7ccdc8cba3a18defedd1a5547
 
38
fa65dad1b0e818e3ccc5cb0e39231352
 
29
ccf804e764ed5650cd8759557269dc13
 
26
465c2e1bee4561cb39e0db8c5993aafc
 
24
Other values (96031)
114857 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83510 ?
Unique (%)72.6%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row128e10d95713541c87cd1a2e48201934
5th row0e7e841ddf8f8f2de2bad69267ecfbcf

Common Values

ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a554738
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e3923135229
 
< 0.1%
ccf804e764ed5650cd8759557269dc1326
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf0124
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec524
 
< 0.1%
c6492b842ac190db807c15aff21a7dd624
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc24
 
< 0.1%
5a3b1c29a49756e75f1ef513383c0c1222
 
< 0.1%
Other values (96026)114739
99.7%

Length

2022-04-06T20:51:53.392595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a554738
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e3923135229
 
< 0.1%
ccf804e764ed5650cd8759557269dc1326
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf0124
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec524
 
< 0.1%
c6492b842ac190db807c15aff21a7dd624
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc24
 
< 0.1%
5a3b1c29a49756e75f1ef513383c0c1222
 
< 0.1%
Other values (96026)114739
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct96036
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
270c23a11d024a44c896d1894b261a83
 
63
13aa59158da63ba0e93ec6ac2c07aacb
 
38
9af2372a1e49340278e7c1ef8d749f34
 
29
92cd3ec6e2d643d4ebd0e3d6238f69e2
 
26
63b964e79dee32a3587651701a2b8dbf
 
24
Other values (96031)
114857 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83510 ?
Unique (%)72.6%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowa20e8105f23924cd00833fd87daa0831
5th row26c7ac168e1433912a51b924fbd34d34

Common Values

ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a8363
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f3429
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e226
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f424
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd292524
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d32924
 
< 0.1%
be1c4e52bb71e0c54b11a26b8e8d59f222
 
< 0.1%
Other values (96026)114739
99.7%

Length

2022-04-06T20:51:53.546588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a8363
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f3429
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e226
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f424
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd292524
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d32924
 
< 0.1%
be1c4e52bb71e0c54b11a26b8e8d59f222
 
< 0.1%
Other values (96026)114739
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
delivered
112654 
shipped
 
1132
canceled
 
530
processing
 
356
invoiced
 
355
Other values (2)
 
10

Length

Max length11
Median length9
Mean length8.975816476
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered112654
97.9%
shipped1132
 
1.0%
canceled530
 
0.5%
processing356
 
0.3%
invoiced355
 
0.3%
unavailable7
 
< 0.1%
approved3
 
< 0.1%

Length

2022-04-06T20:51:53.722612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T20:51:53.843613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
delivered112654
97.9%
shipped1132
 
1.0%
canceled530
 
0.5%
processing356
 
0.3%
invoiced355
 
0.3%
unavailable7
 
< 0.1%
approved3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_purchase_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct95515
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
2017-08-08 20:26:31
 
63
2017-09-23 14:56:45
 
38
2017-04-20 12:45:34
 
29
2017-06-07 12:05:10
 
26
2017-03-09 23:39:26
 
24
Other values (95510)
114857 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82735 ?
Unique (%)71.9%

Sample

1st row2017-10-02 10:56:33
2nd row2017-10-02 10:56:33
3rd row2017-10-02 10:56:33
4th row2017-08-15 18:29:31
5th row2017-08-02 18:24:47

Common Values

ValueCountFrequency (%)
2017-08-08 20:26:3163
 
0.1%
2017-09-23 14:56:4538
 
< 0.1%
2017-04-20 12:45:3429
 
< 0.1%
2017-06-07 12:05:1026
 
< 0.1%
2017-03-09 23:39:2624
 
< 0.1%
2018-05-12 12:28:5824
 
< 0.1%
2017-07-07 14:55:4324
 
< 0.1%
2017-11-25 13:54:3924
 
< 0.1%
2018-02-14 16:34:2724
 
< 0.1%
2017-12-08 12:00:0422
 
< 0.1%
Other values (95505)114739
99.7%

Length

2022-04-06T20:51:53.999589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-241394
 
0.6%
2017-11-25621
 
0.3%
2017-11-27491
 
0.2%
2017-11-26467
 
0.2%
2018-08-06441
 
0.2%
2017-11-28438
 
0.2%
2018-08-07433
 
0.2%
2018-05-15432
 
0.2%
2018-05-07422
 
0.2%
2018-05-14420
 
0.2%
Other values (50661)224515
97.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_approved_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct87916
Distinct (%)76.4%
Missing14
Missing (%)< 0.1%
Memory size898.9 KiB
2017-08-08 20:43:31
 
63
2017-09-25 17:44:41
 
38
2017-04-22 09:10:13
 
29
2017-06-09 16:15:08
 
26
2017-07-07 15:10:17
 
24
Other values (87911)
114843 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70486 ?
Unique (%)61.3%

Sample

1st row2017-10-02 11:07:15
2nd row2017-10-02 11:07:15
3rd row2017-10-02 11:07:15
4th row2017-08-15 20:05:16
5th row2017-08-02 18:43:15

Common Values

ValueCountFrequency (%)
2017-08-08 20:43:3163
 
0.1%
2017-09-25 17:44:4138
 
< 0.1%
2017-04-22 09:10:1329
 
< 0.1%
2017-06-09 16:15:0826
 
< 0.1%
2017-07-07 15:10:1724
 
< 0.1%
2018-05-12 15:41:5824
 
< 0.1%
2017-03-09 23:39:2624
 
< 0.1%
2018-02-21 12:28:1524
 
< 0.1%
2017-11-25 14:16:3424
 
< 0.1%
2018-02-24 03:20:2723
 
< 0.1%
Other values (87906)114724
99.7%

Length

2022-04-06T20:51:54.151611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-241140
 
0.5%
2017-11-24974
 
0.4%
2017-11-25910
 
0.4%
2018-07-05814
 
0.4%
2017-11-28586
 
0.3%
2018-08-07500
 
0.2%
2018-05-08487
 
0.2%
2018-08-20480
 
0.2%
2018-05-01477
 
0.2%
2017-12-05467
 
0.2%
Other values (41645)223211
97.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_delivered_carrier_date
Categorical

HIGH CARDINALITY
MISSING

Distinct78867
Distinct (%)69.3%
Missing1185
Missing (%)1.0%
Memory size898.9 KiB
2017-08-10 11:58:14
 
63
2018-05-09 15:48:00
 
48
2017-10-02 23:47:54
 
38
2018-05-10 18:29:00
 
35
2018-05-14 14:25:00
 
32
Other values (78862)
113636 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59777 ?
Unique (%)52.5%

Sample

1st row2017-10-04 19:55:00
2nd row2017-10-04 19:55:00
3rd row2017-10-04 19:55:00
4th row2017-08-17 15:28:33
5th row2017-08-04 17:35:43

Common Values

ValueCountFrequency (%)
2017-08-10 11:58:1463
 
0.1%
2018-05-09 15:48:0048
 
< 0.1%
2017-10-02 23:47:5438
 
< 0.1%
2018-05-10 18:29:0035
 
< 0.1%
2018-05-14 14:25:0032
 
< 0.1%
2018-05-04 15:46:0029
 
< 0.1%
2017-04-24 11:31:1729
 
< 0.1%
2018-08-08 15:01:0027
 
< 0.1%
2017-06-16 15:50:2826
 
< 0.1%
2017-03-15 11:16:3324
 
< 0.1%
Other values (78857)113501
98.7%
(Missing)1185
 
1.0%

Length

2022-04-06T20:51:54.301594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-28908
 
0.4%
2017-11-27789
 
0.3%
2017-11-29691
 
0.3%
2018-02-27611
 
0.3%
2018-03-27608
 
0.3%
2018-08-06588
 
0.3%
2017-11-30565
 
0.2%
2018-05-14562
 
0.2%
2018-08-13542
 
0.2%
2018-05-03532
 
0.2%
Other values (36934)221308
97.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_delivered_customer_date
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct93239
Distinct (%)82.8%
Missing2384
Missing (%)2.1%
Memory size898.9 KiB
2017-08-14 12:46:18
 
63
2017-10-18 22:35:50
 
38
2017-06-22 16:04:46
 
26
2017-03-21 13:32:45
 
24
2018-06-01 15:18:45
 
24
Other values (93234)
112478 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80365 ?
Unique (%)71.3%

Sample

1st row2017-10-10 21:25:13
2nd row2017-10-10 21:25:13
3rd row2017-10-10 21:25:13
4th row2017-08-18 14:44:43
5th row2017-08-07 18:30:01

Common Values

ValueCountFrequency (%)
2017-08-14 12:46:1863
 
0.1%
2017-10-18 22:35:5038
 
< 0.1%
2017-06-22 16:04:4626
 
< 0.1%
2017-03-21 13:32:4524
 
< 0.1%
2018-06-01 15:18:4524
 
< 0.1%
2018-02-28 20:09:1924
 
< 0.1%
2017-11-30 14:59:1824
 
< 0.1%
2017-07-27 20:52:1524
 
< 0.1%
2017-10-22 14:43:5422
 
< 0.1%
2017-12-21 16:33:1022
 
< 0.1%
Other values (93229)112362
97.7%
(Missing)2384
 
2.1%

Length

2022-04-06T20:51:54.448614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-21520
 
0.2%
2018-05-14518
 
0.2%
2018-08-13502
 
0.2%
2018-05-18497
 
0.2%
2018-08-27496
 
0.2%
2018-05-03488
 
0.2%
2018-04-11481
 
0.2%
2018-08-23477
 
0.2%
2017-12-11477
 
0.2%
2018-04-30475
 
0.2%
Other values (41214)220375
97.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_estimated_delivery_date
Categorical

HIGH CARDINALITY

Distinct449
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
2017-12-20 00:00:00
 
644
2018-03-12 00:00:00
 
602
2018-05-29 00:00:00
 
596
2018-03-13 00:00:00
 
593
2018-07-16 00:00:00
 
586
Other values (444)
112016 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st row2017-10-18 00:00:00
2nd row2017-10-18 00:00:00
3rd row2017-10-18 00:00:00
4th row2017-08-28 00:00:00
5th row2017-08-15 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00644
 
0.6%
2018-03-12 00:00:00602
 
0.5%
2018-05-29 00:00:00596
 
0.5%
2018-03-13 00:00:00593
 
0.5%
2018-07-16 00:00:00586
 
0.5%
2018-07-05 00:00:00582
 
0.5%
2018-05-28 00:00:00576
 
0.5%
2018-05-30 00:00:00575
 
0.5%
2018-02-14 00:00:00570
 
0.5%
2017-12-19 00:00:00564
 
0.5%
Other values (439)109149
94.9%

Length

2022-04-06T20:51:54.593613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00115037
50.0%
2017-12-20644
 
0.3%
2018-03-12602
 
0.3%
2018-05-29596
 
0.3%
2018-03-13593
 
0.3%
2018-07-16586
 
0.3%
2018-07-05582
 
0.3%
2018-05-28576
 
0.3%
2018-05-30575
 
0.2%
2018-02-14570
 
0.2%
Other values (440)109713
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_item_id
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194685188
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:54.745612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6866861517
Coefficient of variation (CV)0.5747841846
Kurtosis93.45279755
Mean1.194685188
Median Absolute Deviation (MAD)0
Skewness7.198477309
Sum137433
Variance0.471537871
MonotonicityNot monotonic
2022-04-06T20:51:54.899612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1100838
87.7%
210000
 
8.7%
32318
 
2.0%
4964
 
0.8%
5456
 
0.4%
6256
 
0.2%
760
 
0.1%
835
 
< 0.1%
928
 
< 0.1%
1025
 
< 0.1%
Other values (11)57
 
< 0.1%
ValueCountFrequency (%)
1100838
87.7%
210000
 
8.7%
32318
 
2.0%
4964
 
0.8%
5456
 
0.4%
6256
 
0.2%
760
 
0.1%
835
 
< 0.1%
928
 
< 0.1%
1025
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
202
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
172
 
< 0.1%
162
 
< 0.1%
154
 
< 0.1%
146
< 0.1%
137
< 0.1%
1212
< 0.1%

product_id
Categorical

HIGH CARDINALITY

Distinct32063
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
531
99a4788cb24856965c36a24e339b6058
 
516
422879e10f46682990de24d770e7f83d
 
506
389d119b48cf3043d311335e499d9c6b
 
404
368c6c730842d78016ad823897a372db
 
392
Other values (32058)
112688 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16892 ?
Unique (%)14.7%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row87285b34884572647811a353c7ac498a
5th row87285b34884572647811a353c7ac498a

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af531
 
0.5%
99a4788cb24856965c36a24e339b6058516
 
0.4%
422879e10f46682990de24d770e7f83d506
 
0.4%
389d119b48cf3043d311335e499d9c6b404
 
0.4%
368c6c730842d78016ad823897a372db392
 
0.3%
53759a2ecddad2bb87a079a1f1519f73389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4353
 
0.3%
53b36df67ebb7c41585e8d54d6772e08323
 
0.3%
154e7e31ebfa092203795c972e5804a6291
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7276
 
0.2%
Other values (32053)111056
96.5%

Length

2022-04-06T20:51:55.072595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af531
 
0.5%
99a4788cb24856965c36a24e339b6058516
 
0.4%
422879e10f46682990de24d770e7f83d506
 
0.4%
389d119b48cf3043d311335e499d9c6b404
 
0.4%
368c6c730842d78016ad823897a372db392
 
0.3%
53759a2ecddad2bb87a079a1f1519f73389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4353
 
0.3%
53b36df67ebb7c41585e8d54d6772e08323
 
0.3%
154e7e31ebfa092203795c972e5804a6291
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7276
 
0.2%
Other values (32053)111056
96.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

seller_id
Categorical

HIGH CARDINALITY

Distinct3021
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
4a3ca9315b744ce9f8e9374361493884
 
2126
6560211a19b47992c3666cc44a7e94c0
 
2105
1f50f920176fa81dab994f9023523100
 
2004
cc419e0650a3c5ba77189a1882b7556a
 
1871
da8622b14eb17ae2831f4ac5b9dab84a
 
1654
Other values (3016)
105277 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique488 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row3504c0cb71d7fa48d967e0e4c94d59d9
5th row3504c0cb71d7fa48d967e0e4c94d59d9

Common Values

ValueCountFrequency (%)
4a3ca9315b744ce9f8e93743614938842126
 
1.8%
6560211a19b47992c3666cc44a7e94c02105
 
1.8%
1f50f920176fa81dab994f90235231002004
 
1.7%
cc419e0650a3c5ba77189a1882b7556a1871
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a1654
 
1.4%
955fee9216a65b617aa5c0531780ce601513
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa1460
 
1.3%
7c67e1448b00f6e969d365cea6b010ab1451
 
1.3%
7a67c85e85bb2ce8582c35f2203ad7361233
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc1229
 
1.1%
Other values (3011)98391
85.5%

Length

2022-04-06T20:51:55.217613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4a3ca9315b744ce9f8e93743614938842126
 
1.8%
6560211a19b47992c3666cc44a7e94c02105
 
1.8%
1f50f920176fa81dab994f90235231002004
 
1.7%
cc419e0650a3c5ba77189a1882b7556a1871
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a1654
 
1.4%
955fee9216a65b617aa5c0531780ce601513
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa1460
 
1.3%
7c67e1448b00f6e969d365cea6b010ab1451
 
1.3%
7a67c85e85bb2ce8582c35f2203ad7361233
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc1229
 
1.1%
Other values (3011)98391
85.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shipping_limit_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct90958
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
2017-08-14 20:43:31
 
63
2017-10-05 17:44:41
 
38
2017-04-27 09:10:13
 
29
2017-06-15 16:15:08
 
26
2018-05-15 15:30:28
 
24
Other values (90953)
114857 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75119 ?
Unique (%)65.3%

Sample

1st row2017-10-06 11:07:15
2nd row2017-10-06 11:07:15
3rd row2017-10-06 11:07:15
4th row2017-08-21 20:05:16
5th row2017-08-08 18:37:31

Common Values

ValueCountFrequency (%)
2017-08-14 20:43:3163
 
0.1%
2017-10-05 17:44:4138
 
< 0.1%
2017-04-27 09:10:1329
 
< 0.1%
2017-06-15 16:15:0826
 
< 0.1%
2018-05-15 15:30:2824
 
< 0.1%
2018-02-27 12:28:1524
 
< 0.1%
2017-07-13 15:10:1724
 
< 0.1%
2017-03-15 23:39:2624
 
< 0.1%
2017-11-30 14:16:3424
 
< 0.1%
2017-10-24 13:06:2122
 
< 0.1%
Other values (90948)114739
99.7%

Length

2022-04-06T20:51:55.371587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-301707
 
0.7%
2017-12-07773
 
0.3%
2018-04-19708
 
0.3%
2018-08-07671
 
0.3%
2018-01-18665
 
0.3%
2018-05-10664
 
0.3%
2018-03-08664
 
0.3%
2018-03-22655
 
0.3%
2018-03-01651
 
0.3%
2018-02-22644
 
0.3%
Other values (40089)222272
96.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5860
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.6500148
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:55.542614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation182.8531136
Coefficient of variation (CV)1.515566442
Kurtosis107.9174333
Mean120.6500148
Median Absolute Deviation (MAD)42
Skewness7.618326263
Sum13879215.75
Variance33435.26114
MonotonicityNot monotonic
2022-04-06T20:51:55.737612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.92561
 
2.2%
69.92089
 
1.8%
49.92005
 
1.7%
89.91598
 
1.4%
99.91491
 
1.3%
29.91355
 
1.2%
39.91321
 
1.1%
79.91263
 
1.1%
19.91253
 
1.1%
29.991202
 
1.0%
Other values (5850)98899
86.0%
ValueCountFrequency (%)
0.853
 
< 0.1%
1.220
< 0.1%
2.22
 
< 0.1%
2.291
 
< 0.1%
2.91
 
< 0.1%
2.991
 
< 0.1%
3.063
 
< 0.1%
3.493
 
< 0.1%
3.57
 
< 0.1%
3.541
 
< 0.1%
ValueCountFrequency (%)
67351
< 0.1%
64991
< 0.1%
47991
< 0.1%
46901
< 0.1%
45901
< 0.1%
4399.871
< 0.1%
4099.991
< 0.1%
40591
< 0.1%
3999.91
< 0.1%
39992
< 0.1%

freight_value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6944
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.04932535
Minimum0
Maximum409.68
Zeros386
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:55.949588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.32
Q321.2
95-th percentile45.332
Maximum409.68
Range409.68
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation15.85042324
Coefficient of variation (CV)0.7905714015
Kurtosis58.28785167
Mean20.04932535
Median Absolute Deviation (MAD)3.63
Skewness5.563999669
Sum2306414.24
Variance251.2359169
MonotonicityNot monotonic
2022-04-06T20:51:56.140614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.13732
 
3.2%
7.782281
 
2.0%
11.851948
 
1.7%
14.11916
 
1.7%
18.231592
 
1.4%
7.391554
 
1.4%
16.111181
 
1.0%
15.231033
 
0.9%
8.72948
 
0.8%
16.79901
 
0.8%
Other values (6934)97951
85.1%
ValueCountFrequency (%)
0386
0.3%
0.014
 
< 0.1%
0.023
 
< 0.1%
0.0314
 
< 0.1%
0.044
 
< 0.1%
0.053
 
< 0.1%
0.0613
 
< 0.1%
0.071
 
< 0.1%
0.0812
 
< 0.1%
0.096
 
< 0.1%
ValueCountFrequency (%)
409.681
< 0.1%
375.282
< 0.1%
339.591
< 0.1%
338.31
< 0.1%
322.11
< 0.1%
321.881
< 0.1%
321.461
< 0.1%
317.471
< 0.1%
314.41
< 0.1%
314.021
< 0.1%

seller_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2203
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24523.88605
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:56.340612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2972
Q16429
median13720
Q328470
95-th percentile88330
Maximum99730
Range98729
Interquartile range (IQR)22041

Descriptive statistics

Standard deviation27644.64337
Coefficient of variation (CV)1.127253785
Kurtosis0.9073104887
Mean24523.88605
Median Absolute Deviation (MAD)8120
Skewness1.548919582
Sum2821154280
Variance764226306.9
MonotonicityNot monotonic
2022-04-06T20:51:56.547588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149408200
 
7.1%
58492119
 
1.8%
150252085
 
1.8%
90151877
 
1.6%
134051665
 
1.4%
85771542
 
1.3%
47821532
 
1.3%
32041460
 
1.3%
41601257
 
1.1%
132321241
 
1.1%
Other values (2193)92059
80.0%
ValueCountFrequency (%)
100122
 
< 0.1%
102140
 
< 0.1%
10225
 
< 0.1%
10235
 
< 0.1%
1026301
0.3%
1031122
0.1%
103517
 
< 0.1%
10391
 
< 0.1%
104021
 
< 0.1%
10412
 
< 0.1%
ValueCountFrequency (%)
9973012
 
< 0.1%
997002
 
< 0.1%
996701
 
< 0.1%
9950061
0.1%
993002
 
< 0.1%
9897519
 
< 0.1%
989201
 
< 0.1%
9891013
 
< 0.1%
9880365
0.1%
987804
 
< 0.1%

seller_city
Categorical

HIGH CARDINALITY

Distinct604
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
sao paulo
28595 
ibitinga
8200 
santo andre
 
3117
curitiba
 
3101
sao jose do rio preto
 
2662
Other values (599)
69362 

Length

Max length40
Median length9
Mean length10.10215844
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.1%

Sample

1st rowmaua
2nd rowmaua
3rd rowmaua
4th rowmaua
5th rowmaua

Common Values

ValueCountFrequency (%)
sao paulo28595
24.9%
ibitinga8200
 
7.1%
santo andre3117
 
2.7%
curitiba3101
 
2.7%
sao jose do rio preto2662
 
2.3%
belo horizonte2578
 
2.2%
rio de janeiro2446
 
2.1%
ribeirao preto2325
 
2.0%
maringa2274
 
2.0%
guarulhos2049
 
1.8%
Other values (594)57690
50.1%

Length

2022-04-06T20:51:56.763613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao35553
 
18.0%
paulo28876
 
14.6%
ibitinga8200
 
4.1%
rio5791
 
2.9%
preto5415
 
2.7%
do5409
 
2.7%
jose4026
 
2.0%
de3908
 
2.0%
santo3235
 
1.6%
andre3132
 
1.6%
Other values (634)94193
47.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

seller_state
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
SP
82041 
PR
8943 
MG
8922 
RJ
 
4891
SC
 
4217
Other values (18)
 
6023

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP82041
71.3%
PR8943
 
7.8%
MG8922
 
7.8%
RJ4891
 
4.3%
SC4217
 
3.7%
RS2216
 
1.9%
DF893
 
0.8%
BA693
 
0.6%
GO535
 
0.5%
PE460
 
0.4%
Other values (13)1226
 
1.1%

Length

2022-04-06T20:51:57.085610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp82041
71.3%
pr8943
 
7.8%
mg8922
 
7.8%
rj4891
 
4.3%
sc4217
 
3.7%
rs2216
 
1.9%
df893
 
0.8%
ba693
 
0.6%
go535
 
0.5%
pe460
 
0.4%
Other values (13)1226
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_unique_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct92939
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
9a736b248f67d166d2fbb006bcb877c3
 
75
6fbc7cdadbb522125f4b27ae9dee4060
 
38
f9ae226291893fda10af7965268fb7f6
 
35
8af7ac63b2efbcbd88e5b11505e8098a
 
29
569aa12b73b5f7edeaa6f2a01603e381
 
26
Other values (92934)
114834 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78991 ?
Unique (%)68.7%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th row3a51803cc0d012c3b5dc8b7528cb05f7
5th rowef0996a1a279c26e7ecbd737be23d235

Common Values

ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c375
 
0.1%
6fbc7cdadbb522125f4b27ae9dee406038
 
< 0.1%
f9ae226291893fda10af7965268fb7f635
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e38126
 
< 0.1%
85963fd37bfd387aa6d915d8a106548624
 
< 0.1%
c8460e4251689ba205045f3ea17884a124
 
< 0.1%
d97b3cfb22b0d6b25ac9ed4e9c2d481b24
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e724
 
< 0.1%
1d2435aa3b858d45c707c9fc25e1877924
 
< 0.1%
Other values (92929)114714
99.7%

Length

2022-04-06T20:51:57.235613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c375
 
0.1%
6fbc7cdadbb522125f4b27ae9dee406038
 
< 0.1%
f9ae226291893fda10af7965268fb7f635
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e38126
 
< 0.1%
85963fd37bfd387aa6d915d8a106548624
 
< 0.1%
c8460e4251689ba205045f3ea17884a124
 
< 0.1%
d97b3cfb22b0d6b25ac9ed4e9c2d481b24
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e724
 
< 0.1%
1d2435aa3b858d45c707c9fc25e1877924
 
< 0.1%
Other values (92929)114714
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14747
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34983.3783
Minimum1003
Maximum99980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:57.409589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3280
Q111095
median24230
Q358297
95-th percentile90620
Maximum99980
Range98977
Interquartile range (IQR)47202

Descriptive statistics

Standard deviation29829.43897
Coefficient of variation (CV)0.852674625
Kurtosis-0.7723390905
Mean34983.3783
Median Absolute Deviation (MAD)16199
Skewness0.7907870136
Sum4024382889
Variance889795429.3
MonotonicityNot monotonic
2022-04-06T20:51:57.612614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220154
 
0.1%
22793151
 
0.1%
22790149
 
0.1%
24230138
 
0.1%
22775126
 
0.1%
35162123
 
0.1%
29101112
 
0.1%
13087107
 
0.1%
11740106
 
0.1%
36570104
 
0.1%
Other values (14737)113767
98.9%
ValueCountFrequency (%)
10031
 
< 0.1%
10042
 
< 0.1%
10056
< 0.1%
10062
 
< 0.1%
10074
< 0.1%
10083
 
< 0.1%
10098
< 0.1%
10116
< 0.1%
10122
 
< 0.1%
10133
 
< 0.1%
ValueCountFrequency (%)
999803
 
< 0.1%
999701
 
< 0.1%
999652
 
< 0.1%
999601
 
< 0.1%
999553
 
< 0.1%
999509
< 0.1%
999402
 
< 0.1%
999305
< 0.1%
999251
 
< 0.1%
999201
 
< 0.1%

customer_city
Categorical

HIGH CARDINALITY

Distinct4044
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
sao paulo
18224 
rio de janeiro
 
7993
belo horizonte
 
3186
brasilia
 
2236
curitiba
 
1786
Other values (4039)
81612 

Length

Max length32
Median length9
Mean length10.33729148
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1004 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowsao paulo
5th rowsao paulo

Common Values

ValueCountFrequency (%)
sao paulo18224
 
15.8%
rio de janeiro7993
 
6.9%
belo horizonte3186
 
2.8%
brasilia2236
 
1.9%
curitiba1786
 
1.6%
campinas1698
 
1.5%
porto alegre1625
 
1.4%
salvador1459
 
1.3%
guarulhos1368
 
1.2%
sao bernardo do campo1094
 
1.0%
Other values (4034)74368
64.6%

Length

2022-04-06T20:51:57.815588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao24557
 
12.2%
paulo18305
 
9.1%
de11227
 
5.6%
rio9596
 
4.8%
janeiro7993
 
4.0%
do4944
 
2.4%
belo3259
 
1.6%
horizonte3214
 
1.6%
brasilia2246
 
1.1%
porto1934
 
1.0%
Other values (3234)114544
56.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_state
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
SP
48683 
RJ
14929 
MG
13386 
RS
6392 
PR
5854 
Other values (22)
25793 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP48683
42.3%
RJ14929
 
13.0%
MG13386
 
11.6%
RS6392
 
5.6%
PR5854
 
5.1%
SC4209
 
3.7%
BA3920
 
3.4%
GO2346
 
2.0%
ES2285
 
2.0%
DF2251
 
2.0%
Other values (17)10782
 
9.4%

Length

2022-04-06T20:51:57.985589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp48683
42.3%
rj14929
 
13.0%
mg13386
 
11.6%
rs6392
 
5.6%
pr5854
 
5.1%
sc4209
 
3.7%
ba3920
 
3.4%
go2346
 
2.0%
es2285
 
2.0%
df2251
 
2.0%
Other values (17)10782
 
9.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

product_category_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
cama_mesa_banho
11819 
beleza_saude
9914 
esporte_lazer
8925 
moveis_decoracao
8723 
informatica_acessorios
8075 
Other values (66)
67581 

Length

Max length46
Median length15
Mean length14.88322887
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutilidades_domesticas
2nd rowutilidades_domesticas
3rd rowutilidades_domesticas
4th rowutilidades_domesticas
5th rowutilidades_domesticas

Common Values

ValueCountFrequency (%)
cama_mesa_banho11819
 
10.3%
beleza_saude9914
 
8.6%
esporte_lazer8925
 
7.8%
moveis_decoracao8723
 
7.6%
informatica_acessorios8075
 
7.0%
utilidades_domesticas7312
 
6.4%
relogios_presentes6136
 
5.3%
telefonia4661
 
4.1%
ferramentas_jardim4547
 
4.0%
automotivo4346
 
3.8%
Other values (61)40579
35.3%

Length

2022-04-06T20:51:58.153613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cama_mesa_banho11819
 
10.3%
beleza_saude9914
 
8.6%
esporte_lazer8925
 
7.8%
moveis_decoracao8723
 
7.6%
informatica_acessorios8075
 
7.0%
utilidades_domesticas7312
 
6.4%
relogios_presentes6136
 
5.3%
telefonia4661
 
4.1%
ferramentas_jardim4547
 
4.0%
automotivo4346
 
3.8%
Other values (61)40579
35.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

product_name_lenght
Real number (ℝ≥0)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.75428775
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:58.337612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.03833567
Coefficient of variation (CV)0.2058964684
Kurtosis0.1464021057
Mean48.75428775
Median Absolute Deviation (MAD)6
Skewness-0.9041148344
Sum5608547
Variance100.768183
MonotonicityNot monotonic
2022-04-06T20:51:58.542589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
598570
 
7.4%
607904
 
6.9%
566775
 
5.9%
586712
 
5.8%
576219
 
5.4%
555773
 
5.0%
545443
 
4.7%
534306
 
3.7%
524242
 
3.7%
493659
 
3.2%
Other values (56)55434
48.2%
ValueCountFrequency (%)
59
 
< 0.1%
63
 
< 0.1%
72
 
< 0.1%
84
 
< 0.1%
914
 
< 0.1%
108
 
< 0.1%
1111
 
< 0.1%
1237
< 0.1%
1326
< 0.1%
1446
< 0.1%
ValueCountFrequency (%)
761
 
< 0.1%
729
 
< 0.1%
691
 
< 0.1%
681
 
< 0.1%
673
 
< 0.1%
661
 
< 0.1%
64169
 
0.1%
631332
1.2%
62160
 
0.1%
61238
 
0.2%

product_description_lenght
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2958
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.867573
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:58.758614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile159.8
Q1345
median600
Q3982
95-th percentile2125.2
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation653.0514924
Coefficient of variation (CV)0.8309943238
Kurtosis4.91709565
Mean785.867573
Median Absolute Deviation (MAD)296
Skewness2.011066015
Sum90403848
Variance426476.2518
MonotonicityNot monotonic
2022-04-06T20:51:58.959612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341706
 
0.6%
1893663
 
0.6%
348642
 
0.6%
492588
 
0.5%
903586
 
0.5%
245575
 
0.5%
366529
 
0.5%
236510
 
0.4%
340483
 
0.4%
919436
 
0.4%
Other values (2948)109319
95.0%
ValueCountFrequency (%)
46
< 0.1%
82
 
< 0.1%
151
 
< 0.1%
207
< 0.1%
231
 
< 0.1%
262
 
< 0.1%
274
< 0.1%
282
 
< 0.1%
308
< 0.1%
312
 
< 0.1%
ValueCountFrequency (%)
39922
 
< 0.1%
39881
 
< 0.1%
39853
< 0.1%
39766
< 0.1%
39631
 
< 0.1%
39563
< 0.1%
39542
 
< 0.1%
39502
 
< 0.1%
39491
 
< 0.1%
39481
 
< 0.1%

product_photos_qty
Real number (ℝ≥0)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.200318159
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:59.143612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.713212606
Coefficient of variation (CV)0.7786204006
Kurtosis4.907432821
Mean2.200318159
Median Absolute Deviation (MAD)0
Skewness1.919275288
Sum253118
Variance2.935097433
MonotonicityNot monotonic
2022-04-06T20:51:59.300610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
158264
50.6%
222763
 
19.8%
312791
 
11.1%
48720
 
7.6%
55540
 
4.8%
63872
 
3.4%
71463
 
1.3%
8766
 
0.7%
10351
 
0.3%
9309
 
0.3%
Other values (9)198
 
0.2%
ValueCountFrequency (%)
158264
50.6%
222763
 
19.8%
312791
 
11.1%
48720
 
7.6%
55540
 
4.8%
63872
 
3.4%
71463
 
1.3%
8766
 
0.7%
9309
 
0.3%
10351
 
0.3%
ValueCountFrequency (%)
201
 
< 0.1%
192
 
< 0.1%
184
 
< 0.1%
1711
 
< 0.1%
1512
 
< 0.1%
146
 
< 0.1%
1330
 
< 0.1%
1260
 
0.1%
1172
 
0.1%
10351
0.3%

product_weight_g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2197
Distinct (%)1.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2112.365746
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:59.492588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3776.693083
Coefficient of variation (CV)1.787897332
Kurtosis15.932725
Mean2112.365746
Median Absolute Deviation (MAD)500
Skewness3.570368112
Sum242998106
Variance14263410.64
MonotonicityNot monotonic
2022-04-06T20:51:59.697588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006788
 
5.9%
1505295
 
4.6%
2504657
 
4.0%
3004295
 
3.7%
1003559
 
3.1%
4003460
 
3.0%
3503204
 
2.8%
5002775
 
2.4%
6002763
 
2.4%
7002083
 
1.8%
Other values (2187)76157
66.2%
ValueCountFrequency (%)
08
 
< 0.1%
25
 
< 0.1%
253
 
< 0.1%
50982
0.9%
532
 
< 0.1%
542
 
< 0.1%
552
 
< 0.1%
581
 
< 0.1%
609
 
< 0.1%
615
 
< 0.1%
ValueCountFrequency (%)
404253
 
< 0.1%
30000285
0.2%
298001
 
< 0.1%
297501
 
< 0.1%
297004
 
< 0.1%
296005
 
< 0.1%
295002
 
< 0.1%
292501
 
< 0.1%
291501
 
< 0.1%
291001
 
< 0.1%

product_length_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.30089711
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:51:59.912613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.20368427
Coefficient of variation (CV)0.5347592256
Kurtosis3.668261707
Mean30.30089711
Median Absolute Deviation (MAD)8
Skewness1.74206068
Sum3485694
Variance262.5593841
MonotonicityNot monotonic
2022-04-06T20:52:00.125612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1617652
 
15.3%
2010556
 
9.2%
307746
 
6.7%
176103
 
5.3%
185826
 
5.1%
194832
 
4.2%
254750
 
4.1%
404230
 
3.7%
223911
 
3.4%
503081
 
2.7%
Other values (89)46349
40.3%
ValueCountFrequency (%)
732
 
< 0.1%
82
 
< 0.1%
94
 
< 0.1%
108
 
< 0.1%
1196
 
0.1%
1240
 
< 0.1%
1360
 
0.1%
14136
 
0.1%
15212
 
0.2%
1617652
15.3%
ValueCountFrequency (%)
105329
0.3%
10430
 
< 0.1%
10345
 
< 0.1%
10260
 
0.1%
101107
 
0.1%
100423
0.4%
9936
 
< 0.1%
9849
 
< 0.1%
9711
 
< 0.1%
968
 
< 0.1%

product_height_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.65871553
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:52:00.332587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.48410769
Coefficient of variation (CV)0.809432616
Kurtosis7.274135572
Mean16.65871553
Median Absolute Deviation (MAD)6
Skewness2.240967679
Sum1916352
Variance181.8211603
MonotonicityNot monotonic
2022-04-06T20:52:00.694588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010097
 
8.8%
206775
 
5.9%
156763
 
5.9%
116250
 
5.4%
126172
 
5.4%
25039
 
4.4%
44757
 
4.1%
84747
 
4.1%
164650
 
4.0%
54613
 
4.0%
Other values (92)55173
48.0%
ValueCountFrequency (%)
25039
4.4%
32761
 
2.4%
44757
4.1%
54613
4.0%
63490
 
3.0%
74185
3.6%
84747
4.1%
93360
 
2.9%
1010097
8.8%
116250
5.4%
ValueCountFrequency (%)
105137
0.1%
10414
 
< 0.1%
10349
 
< 0.1%
10210
 
< 0.1%
10042
 
< 0.1%
995
 
< 0.1%
983
 
< 0.1%
972
 
< 0.1%
968
 
< 0.1%
9522
 
< 0.1%

product_width_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.10408915
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:52:00.897614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.73032474
Coefficient of variation (CV)0.5077163898
Kurtosis4.532601408
Mean23.10408915
Median Absolute Deviation (MAD)6
Skewness1.699763004
Sum2657802
Variance137.6005184
MonotonicityNot monotonic
2022-04-06T20:52:01.097613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012401
 
10.8%
1110622
 
9.2%
158935
 
7.8%
168653
 
7.5%
307819
 
6.8%
125568
 
4.8%
135384
 
4.7%
144723
 
4.1%
184034
 
3.5%
404034
 
3.5%
Other values (85)42863
37.3%
ValueCountFrequency (%)
62
 
< 0.1%
75
 
< 0.1%
829
 
< 0.1%
951
 
< 0.1%
1083
 
0.1%
1110622
9.2%
125568
4.8%
135384
4.7%
144723
4.1%
158935
7.8%
ValueCountFrequency (%)
1187
 
< 0.1%
10514
 
< 0.1%
1041
 
< 0.1%
1031
 
< 0.1%
1022
 
< 0.1%
1012
 
< 0.1%
10043
< 0.1%
981
 
< 0.1%
971
 
< 0.1%
952
 
< 0.1%

payment_sequential
Real number (ℝ≥0)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.094056695
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:52:01.286613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7315441733
Coefficient of variation (CV)0.6686528923
Kurtosis348.8888491
Mean1.094056695
Median Absolute Deviation (MAD)0
Skewness15.96822497
Sum125857
Variance0.5351568775
MonotonicityNot monotonic
2022-04-06T20:52:01.445611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1110108
95.7%
23280
 
2.9%
3636
 
0.6%
4307
 
0.3%
5183
 
0.2%
6127
 
0.1%
788
 
0.1%
858
 
0.1%
947
 
< 0.1%
1040
 
< 0.1%
Other values (19)163
 
0.1%
ValueCountFrequency (%)
1110108
95.7%
23280
 
2.9%
3636
 
0.6%
4307
 
0.3%
5183
 
0.2%
6127
 
0.1%
788
 
0.1%
858
 
0.1%
947
 
< 0.1%
1040
 
< 0.1%
ValueCountFrequency (%)
291
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
223
< 0.1%
216
< 0.1%
206
< 0.1%

payment_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
credit_card
84834 
boleto
22406 
voucher
 
6142
debit_card
 
1655

Length

Max length11
Median length11
Mean length9.79818667
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card84834
73.7%
boleto22406
 
19.5%
voucher6142
 
5.3%
debit_card1655
 
1.4%

Length

2022-04-06T20:52:01.674586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T20:52:01.795587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
credit_card84834
73.7%
boleto22406
 
19.5%
voucher6142
 
5.3%
debit_card1655
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

payment_installments
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.944565661
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:52:01.928588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.780108304
Coefficient of variation (CV)0.9441488572
Kurtosis2.529947266
Mean2.944565661
Median Absolute Deviation (MAD)1
Skewness1.620915505
Sum338734
Variance7.729002184
MonotonicityNot monotonic
2022-04-06T20:52:02.128590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
157328
49.8%
213352
 
11.6%
311498
 
10.0%
47820
 
6.8%
106740
 
5.9%
55888
 
5.1%
84978
 
4.3%
64519
 
3.9%
71779
 
1.5%
9708
 
0.6%
Other values (14)427
 
0.4%
ValueCountFrequency (%)
03
 
< 0.1%
157328
49.8%
213352
 
11.6%
311498
 
10.0%
47820
 
6.8%
55888
 
5.1%
64519
 
3.9%
71779
 
1.5%
84978
 
4.3%
9708
 
0.6%
ValueCountFrequency (%)
2434
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
216
 
< 0.1%
2020
 
< 0.1%
1838
< 0.1%
177
 
< 0.1%
167
 
< 0.1%
1591
0.1%
1416
 
< 0.1%

payment_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28598
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.4092939
Minimum0
Maximum13664.08
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size898.9 KiB
2022-04-06T20:52:02.331591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.19
Q160.85
median108
Q3189.43
95-th percentile515.1
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.58

Descriptive statistics

Standard deviation266.2045083
Coefficient of variation (CV)1.54402644
Kurtosis524.9065678
Mean172.4092939
Median Absolute Deviation (MAD)56.75
Skewness14.31714272
Sum19833447.94
Variance70864.84021
MonotonicityNot monotonic
2022-04-06T20:52:02.554590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50337
 
0.3%
20283
 
0.2%
100282
 
0.2%
77.57247
 
0.2%
35162
 
0.1%
73.34157
 
0.1%
30133
 
0.1%
116.94130
 
0.1%
56.78120
 
0.1%
155.14119
 
0.1%
Other values (28588)113067
98.3%
ValueCountFrequency (%)
06
< 0.1%
0.016
< 0.1%
0.032
 
< 0.1%
0.052
 
< 0.1%
0.082
 
< 0.1%
0.091
 
< 0.1%
0.13
< 0.1%
0.112
 
< 0.1%
0.131
 
< 0.1%
0.144
< 0.1%
ValueCountFrequency (%)
13664.088
< 0.1%
7274.884
< 0.1%
6929.311
 
< 0.1%
6726.661
 
< 0.1%
6081.546
< 0.1%
4950.341
 
< 0.1%
4809.442
 
< 0.1%
4764.341
 
< 0.1%
4681.781
 
< 0.1%
4513.321
 
< 0.1%

product_category_name_english
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
bed_bath_table
11819 
health_beauty
9914 
sports_leisure
8925 
furniture_decor
8723 
computers_accessories
8075 
Other values (66)
67581 

Length

Max length39
Median length13
Mean length12.99040309
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowhousewares
3rd rowhousewares
4th rowhousewares
5th rowhousewares

Common Values

ValueCountFrequency (%)
bed_bath_table11819
 
10.3%
health_beauty9914
 
8.6%
sports_leisure8925
 
7.8%
furniture_decor8723
 
7.6%
computers_accessories8075
 
7.0%
housewares7312
 
6.4%
watches_gifts6136
 
5.3%
telephony4661
 
4.1%
garden_tools4547
 
4.0%
auto4346
 
3.8%
Other values (61)40579
35.3%

Length

2022-04-06T20:52:02.796586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bed_bath_table11819
 
10.3%
health_beauty9914
 
8.6%
sports_leisure8925
 
7.8%
furniture_decor8723
 
7.6%
computers_accessories8075
 
7.0%
housewares7312
 
6.4%
watches_gifts6136
 
5.3%
telephony4661
 
4.1%
garden_tools4547
 
4.0%
auto4346
 
3.8%
Other values (61)40579
35.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

geolocation_lat_customer
Real number (ℝ)

HIGH CORRELATION

Distinct14743
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-21.23576548
Minimum-36.60537441
Maximum42.18400274
Zeros0
Zeros (%)0.0%
Negative114914
Negative (%)99.9%
Memory size898.9 KiB
2022-04-06T20:52:03.046610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-36.60537441
5-th percentile-28.62869711
Q1-23.59002278
median-22.92991227
Q3-20.19822232
95-th percentile-7.97347196
Maximum42.18400274
Range78.78937715
Interquartile range (IQR)3.391800461

Descriptive statistics

Standard deviation5.571870904
Coefficient of variation (CV)-0.2623814483
Kurtosis3.501648759
Mean-21.23576548
Median Absolute Deviation (MAD)0.8913251649
Skewness1.663469957
Sum-2442898.754
Variance31.04574537
MonotonicityNot monotonic
2022-04-06T20:52:03.295588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.90581655154
 
0.1%
-23.00927096151
 
0.1%
-23.01133469149
 
0.1%
-22.91127038138
 
0.1%
-22.97295652126
 
0.1%
-19.47809554123
 
0.1%
-20.33161264112
 
0.1%
-22.85275771107
 
0.1%
-24.14965101106
 
0.1%
-20.72193768104
 
0.1%
Other values (14733)113767
98.9%
ValueCountFrequency (%)
-36.6053744112
< 0.1%
-34.586422111
 
< 0.1%
-33.691422925
 
< 0.1%
-33.5256005715
< 0.1%
-32.56395169
< 0.1%
-32.231278784
 
< 0.1%
-32.205217171
 
< 0.1%
-32.204285233
 
< 0.1%
-32.188846254
 
< 0.1%
-32.188109952
 
< 0.1%
ValueCountFrequency (%)
42.184002741
 
< 0.1%
41.146202912
 
< 0.1%
39.057629422
 
< 0.1%
3.8449014911
 
< 0.1%
3.3582320381
 
< 0.1%
2.8599291954
< 0.1%
2.844624751
 
< 0.1%
2.8338823113
< 0.1%
2.8301595865
< 0.1%
2.8241440711
 
< 0.1%

geolocation_lng_customer
Real number (ℝ)

HIGH CORRELATION

Distinct14744
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-46.19785734
Minimum-72.66670555
Maximum-8.577855018
Zeros0
Zeros (%)0.0%
Negative115037
Negative (%)100.0%
Memory size898.9 KiB
2022-04-06T20:52:03.543587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-72.66670555
5-th percentile-52.38356841
Q1-48.101695
median-46.632021
Q3-43.62599281
95-th percentile-38.5059849
Maximum-8.577855018
Range64.08885053
Interquartile range (IQR)4.475702196

Descriptive statistics

Standard deviation4.05064469
Coefficient of variation (CV)-0.08768035843
Kurtosis2.271704084
Mean-46.19785734
Median Absolute Deviation (MAD)2.439993631
Skewness0.02812258526
Sum-5314462.915
Variance16.40772241
MonotonicityNot monotonic
2022-04-06T20:52:03.781585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.10698886154
 
0.1%
-43.42940928151
 
0.1%
-43.45025642149
 
0.1%
-43.10515134138
 
0.1%
-43.39706328126
 
0.1%
-42.55679483123
 
0.1%
-40.27958609112
 
0.1%
-47.05510214107
 
0.1%
-46.7480979106
 
0.1%
-42.8660116104
 
0.1%
Other values (14734)113767
98.9%
ValueCountFrequency (%)
-72.666705553
 
< 0.1%
-69.25905191
 
< 0.1%
-68.74200323
 
< 0.1%
-68.74094121
 
< 0.1%
-68.508006992
 
< 0.1%
-67.884269651
 
< 0.1%
-67.851510778
< 0.1%
-67.843456516
< 0.1%
-67.837869469
< 0.1%
-67.8351172513
< 0.1%
ValueCountFrequency (%)
-8.5778550182
 
< 0.1%
-8.7237621481
 
< 0.1%
-9.4000369252
 
< 0.1%
-34.800340361
 
< 0.1%
-34.808186361
 
< 0.1%
-34.824583637
< 0.1%
-34.8246729514
< 0.1%
-34.825280626
< 0.1%
-34.825605112
 
< 0.1%
-34.826440697
< 0.1%

geolocation_lat_seller
Real number (ℝ)

HIGH CORRELATION

Distinct2203
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.79605947
Minimum-36.60537441
Maximum-2.546079234
Zeros0
Zeros (%)0.0%
Negative115037
Negative (%)100.0%
Memory size898.9 KiB
2022-04-06T20:52:04.020588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-36.60537441
5-th percentile-26.33054814
Q1-23.61165419
median-23.42073909
Q3-21.76647685
95-th percentile-19.8498827
Maximum-2.546079234
Range34.05929518
Interquartile range (IQR)1.845177347

Descriptive statistics

Standard deviation2.696611906
Coefficient of variation (CV)-0.1182928966
Kurtosis18.18706634
Mean-22.79605947
Median Absolute Deviation (MAD)0.5381883235
Skewness2.764194626
Sum-2622390.294
Variance7.271715773
MonotonicityNot monotonic
2022-04-06T20:52:04.224589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-21.766476858200
 
7.1%
-23.651114842119
 
1.8%
-20.802436262085
 
1.8%
-23.665703461877
 
1.6%
-22.716839291665
 
1.4%
-23.482623441542
 
1.3%
-23.6939861532
 
1.3%
-23.597985521460
 
1.3%
-23.623581551257
 
1.1%
-23.207064311241
 
1.1%
Other values (2193)92059
80.0%
ValueCountFrequency (%)
-36.605374414
 
< 0.1%
-32.0795133818
 
< 0.1%
-31.772412872
 
< 0.1%
-31.321519171
 
< 0.1%
-30.159469376
0.1%
-30.110020919
 
< 0.1%
-30.103303726
 
< 0.1%
-30.099819435
 
< 0.1%
-30.080613515
 
< 0.1%
-30.0720082448
< 0.1%
ValueCountFrequency (%)
-2.546079234401
0.3%
-3.1356226833
 
< 0.1%
-3.71903659814
 
< 0.1%
-3.7233061293
 
< 0.1%
-3.7236724471
 
< 0.1%
-3.7439878994
 
< 0.1%
-3.7592095071
 
< 0.1%
-3.76992334
 
< 0.1%
-3.78036099512
 
< 0.1%
-3.78381482716
 
< 0.1%

geolocation_lng_seller
Real number (ℝ)

HIGH CORRELATION

Distinct2203
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-47.24790942
Minimum-67.8096558
Maximum-34.84785618
Zeros0
Zeros (%)0.0%
Negative115037
Negative (%)100.0%
Memory size898.9 KiB
2022-04-06T20:52:04.623598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-67.8096558
5-th percentile-51.39151634
Q1-48.83154738
median-46.75521082
Q3-46.51808197
95-th percentile-43.30459253
Maximum-34.84785618
Range32.96179962
Interquartile range (IQR)2.313465411

Descriptive statistics

Standard deviation2.346335751
Coefficient of variation (CV)-0.0496600967
Kurtosis4.851139313
Mean-47.24790942
Median Absolute Deviation (MAD)0.7668289084
Skewness0.5590092336
Sum-5435257.755
Variance5.505291455
MonotonicityNot monotonic
2022-04-06T20:52:04.844592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-48.831547388200
 
7.1%
-46.755210822119
 
1.8%
-49.395624072085
 
1.8%
-46.518081971877
 
1.6%
-47.657365851665
 
1.4%
-46.374489531542
 
1.3%
-46.701883241532
 
1.3%
-46.555472791460
 
1.3%
-46.610560071257
 
1.1%
-46.760735211241
 
1.1%
Other values (2193)92059
80.0%
ValueCountFrequency (%)
-67.80965581
 
< 0.1%
-64.283946464
 
< 0.1%
-63.887972996
 
< 0.1%
-61.957201168
 
< 0.1%
-60.023468883
 
< 0.1%
-57.086320421
 
< 0.1%
-56.1029601452
< 0.1%
-56.0679723337
< 0.1%
-55.4964428956
< 0.1%
-54.969893182
 
< 0.1%
ValueCountFrequency (%)
-34.847856181
 
< 0.1%
-34.85576256
 
< 0.1%
-34.896786293
 
< 0.1%
-34.8985310814
 
< 0.1%
-34.901552851
 
< 0.1%
-34.918399396
 
< 0.1%
-34.93222646384
0.3%
-35.1250923815
 
< 0.1%
-35.2016432123
 
< 0.1%
-35.2082602716
 
< 0.1%

review_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct95840
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
eef5dbca8d37dfce6db7d7b16dd0525e
 
63
7145a6f0d38ec713897856cbdcfcdb7f
 
38
f28281373ab8815bafafe371218f02ce
 
29
8823bba1e3301fee652eb06de8ef9435
 
26
b5292206f96cd5d97359940203a0b510
 
24
Other values (95835)
114857 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83179 ?
Unique (%)72.3%

Sample

1st rowa54f0611adc9ed256b57ede6b6eb5114
2nd rowa54f0611adc9ed256b57ede6b6eb5114
3rd rowa54f0611adc9ed256b57ede6b6eb5114
4th rowb46f1e34512b0f4c74a72398b03ca788
5th rowdc90f19c2806f1abba9e72ad3c350073

Common Values

ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f38
 
< 0.1%
f28281373ab8815bafafe371218f02ce29
 
< 0.1%
8823bba1e3301fee652eb06de8ef943526
 
< 0.1%
b5292206f96cd5d97359940203a0b51024
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e24
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf63424
 
< 0.1%
b79b22bb50f78f1afe361661011fd89224
 
< 0.1%
7e568736c98c553aea896a5dca746d5a22
 
< 0.1%
6fe49ee0a2b00dddf7ebddb5847f928321
 
< 0.1%
Other values (95830)114742
99.7%

Length

2022-04-06T20:52:05.076589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f38
 
< 0.1%
f28281373ab8815bafafe371218f02ce29
 
< 0.1%
8823bba1e3301fee652eb06de8ef943526
 
< 0.1%
b5292206f96cd5d97359940203a0b51024
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e24
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf63424
 
< 0.1%
b79b22bb50f78f1afe361661011fd89224
 
< 0.1%
7e568736c98c553aea896a5dca746d5a22
 
< 0.1%
e8236fe7b6e1bdd513a500de361e2b8721
 
< 0.1%
Other values (95830)114742
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
5
65048 
4
21844 
1
14475 
3
9665 
2
 
4005

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row5

Common Values

ValueCountFrequency (%)
565048
56.5%
421844
 
19.0%
114475
 
12.6%
39665
 
8.4%
24005
 
3.5%

Length

2022-04-06T20:52:05.254588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T20:52:05.360590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
565048
56.5%
421844
 
19.0%
114475
 
12.6%
39665
 
8.4%
24005
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

review_comment_title
Categorical

HIGH CARDINALITY
MISSING

Distinct4455
Distinct (%)32.4%
Missing101302
Missing (%)88.1%
Memory size898.9 KiB
Recomendo
 
487
recomendo
 
400
Bom
 
327
super recomendo
 
309
Excelente
 
291
Other values (4450)
11921 

Length

Max length26
Median length11
Mean length12.18340007
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3049 ?
Unique (%)22.2%

Sample

1st rowCortina de qualidade
2nd rowCumpriu com o pro
3rd rowpessimo atendimento
4th rowMuito bom os produtos
5th rowMuito bom os produtos

Common Values

ValueCountFrequency (%)
Recomendo487
 
0.4%
recomendo400
 
0.3%
Bom327
 
0.3%
super recomendo309
 
0.3%
Excelente291
 
0.3%
Muito bom278
 
0.2%
Ótimo267
 
0.2%
Super recomendo252
 
0.2%
Ótimo 234
 
0.2%
Otimo201
 
0.2%
Other values (4445)10689
 
9.3%
(Missing)101302
88.1%

Length

2022-04-06T20:52:05.513613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
recomendo2431
 
9.3%
produto1534
 
5.9%
bom1502
 
5.7%
muito1033
 
3.9%
super1028
 
3.9%
não901
 
3.4%
ótimo797
 
3.0%
excelente761
 
2.9%
entrega691
 
2.6%
recebi429
 
1.6%
Other values (2070)15100
57.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

review_comment_message
Categorical

HIGH CARDINALITY
MISSING

Distinct35020
Distinct (%)71.9%
Missing66357
Missing (%)57.7%
Memory size898.9 KiB
Muito bom
 
253
Bom
 
206
muito bom
 
133
bom
 
115
Otimo
 
114
Other values (35015)
47859 

Length

Max length208
Median length54
Mean length70.24172145
Min length1

Characters and Unicode

Total characters6736
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28603 ?
Unique (%)58.8%

Sample

1st rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2nd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3rd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
4th rowDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.
5th rowSó achei ela pequena pra seis xícaras ,mais é um bom produto

Common Values

ValueCountFrequency (%)
Muito bom253
 
0.2%
Bom206
 
0.2%
muito bom133
 
0.1%
bom115
 
0.1%
Otimo114
 
0.1%
Recomendo109
 
0.1%
otimo103
 
0.1%
Ok86
 
0.1%
Ótimo85
 
0.1%
Ótimo 82
 
0.1%
Other values (35010)47394
41.2%
(Missing)66357
57.7%

Length

2022-04-06T20:52:05.721612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o21976
 
3.8%
produto20397
 
3.5%
e19319
 
3.3%
a14683
 
2.5%
de14025
 
2.4%
do12741
 
2.2%
não12650
 
2.2%
que10090
 
1.7%
prazo9213
 
1.6%
muito8894
 
1.5%
Other values (19275)439767
75.3%

Most occurring characters

ValueCountFrequency (%)
6736
100.0%

Most occurring categories

ValueCountFrequency (%)
Control6736
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
6736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6736
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6736
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6736
100.0%

review_creation_date
Categorical

HIGH CARDINALITY

Distinct632
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
2017-12-19 00:00:00
 
531
2018-05-22 00:00:00
 
515
2018-05-15 00:00:00
 
514
2017-12-20 00:00:00
 
502
2018-05-19 00:00:00
 
500
Other values (627)
112475 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row2017-10-11 00:00:00
2nd row2017-10-11 00:00:00
3rd row2017-10-11 00:00:00
4th row2017-08-19 00:00:00
5th row2017-08-08 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-19 00:00:00531
 
0.5%
2018-05-22 00:00:00515
 
0.4%
2018-05-15 00:00:00514
 
0.4%
2017-12-20 00:00:00502
 
0.4%
2018-05-19 00:00:00500
 
0.4%
2018-08-28 00:00:00497
 
0.4%
2018-05-04 00:00:00495
 
0.4%
2018-03-29 00:00:00490
 
0.4%
2018-03-30 00:00:00477
 
0.4%
2018-08-14 00:00:00476
 
0.4%
Other values (622)110040
95.7%

Length

2022-04-06T20:52:05.895595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00114942
50.0%
2017-12-19531
 
0.2%
2018-05-22515
 
0.2%
2018-05-15514
 
0.2%
2017-12-20502
 
0.2%
2018-05-19500
 
0.2%
2018-08-28497
 
0.2%
2018-05-04495
 
0.2%
2018-03-29490
 
0.2%
2018-03-30477
 
0.2%
Other values (624)110611
48.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

review_answer_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct95685
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size898.9 KiB
2017-08-17 22:17:55
 
63
2017-10-19 21:08:44
 
38
2017-05-24 16:21:27
 
29
2017-06-28 18:49:50
 
26
2018-03-07 15:08:10
 
24
Other values (95680)
114857 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82913 ?
Unique (%)72.1%

Sample

1st row2017-10-12 03:43:48
2nd row2017-10-12 03:43:48
3rd row2017-10-12 03:43:48
4th row2017-08-20 15:16:36
5th row2017-08-08 23:26:23

Common Values

ValueCountFrequency (%)
2017-08-17 22:17:5563
 
0.1%
2017-10-19 21:08:4438
 
< 0.1%
2017-05-24 16:21:2729
 
< 0.1%
2017-06-28 18:49:5026
 
< 0.1%
2018-03-07 15:08:1024
 
< 0.1%
2017-03-23 08:34:1324
 
< 0.1%
2017-08-01 13:20:3124
 
< 0.1%
2018-06-04 19:04:2024
 
< 0.1%
2017-12-22 20:33:5222
 
< 0.1%
2018-06-15 19:25:5521
 
< 0.1%
Other values (95675)114742
99.7%

Length

2022-04-06T20:52:06.046589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-20768
 
0.3%
2018-05-21677
 
0.3%
2018-05-10582
 
0.3%
2017-12-20464
 
0.2%
2018-04-13421
 
0.2%
2018-05-11410
 
0.2%
2018-08-24410
 
0.2%
2017-12-13410
 
0.2%
2017-12-22407
 
0.2%
2017-12-21402
 
0.2%
Other values (53254)225123
97.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-06T20:51:41.613118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:21.163224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:26.136096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:31.282094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:36.206119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:40.800096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:45.274105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:49.678101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:54.148101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:58.503101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:03.254093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:07.746099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:11.797121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:16.006101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:20.119095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:24.183094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:28.521117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:33.036119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:37.456098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:41.836101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:21.752094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:26.395098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:31.516098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:36.449095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:41.065099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:45.503101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:49.918123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:54.529121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:58.736096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:03.492093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:07.965097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:12.021121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:16.223118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:20.340096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:24.410120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:28.752100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:33.264114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:37.686120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:51:42.059118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:21.996095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:26.645096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:31.765095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:36.677121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:41.291119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:45.721102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:50.158096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:54.750095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-06T20:50:25.118095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:30.223101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-06T20:50:34.977099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

2022-04-06T20:52:06.296403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-06T20:52:06.796891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-06T20:52:07.208884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-06T20:52:07.582885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-06T20:52:07.849912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-06T20:51:46.328751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-06T20:51:48.899079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-06T20:51:50.756478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-06T20:51:51.599699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

order_idcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_dateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valueseller_zip_code_prefixseller_cityseller_statecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmpayment_sequentialpayment_typepayment_installmentspayment_valueproduct_category_name_englishgeolocation_lat_customergeolocation_lng_customergeolocation_lat_sellergeolocation_lng_sellerreview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestamp
0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:00187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.729350mauaSP7c396fd4830fd04220f754e42b4e5bff3149sao pauloSPutilidades_domesticas40.0268.04.0500.019.08.013.01credit_card118.12housewares-23.574809-46.587471-23.680114-46.452454a54f0611adc9ed256b57ede6b6eb51144NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:48
1e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:00187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.729350mauaSP7c396fd4830fd04220f754e42b4e5bff3149sao pauloSPutilidades_domesticas40.0268.04.0500.019.08.013.03voucher12.00housewares-23.574809-46.587471-23.680114-46.452454a54f0611adc9ed256b57ede6b6eb51144NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:48
2e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:00187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.729350mauaSP7c396fd4830fd04220f754e42b4e5bff3149sao pauloSPutilidades_domesticas40.0268.04.0500.019.08.013.02voucher118.59housewares-23.574809-46.587471-23.680114-46.452454a54f0611adc9ed256b57ede6b6eb51144NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:48
3128e10d95713541c87cd1a2e48201934a20e8105f23924cd00833fd87daa0831delivered2017-08-15 18:29:312017-08-15 20:05:162017-08-17 15:28:332017-08-18 14:44:432017-08-28 00:00:00187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-21 20:05:1629.997.789350mauaSP3a51803cc0d012c3b5dc8b7528cb05f73366sao pauloSPutilidades_domesticas40.0268.04.0500.019.08.013.01credit_card337.77housewares-23.565578-46.534603-23.680114-46.452454b46f1e34512b0f4c74a72398b03ca7884NaNDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.2017-08-19 00:00:002017-08-20 15:16:36
40e7e841ddf8f8f2de2bad69267ecfbcf26c7ac168e1433912a51b924fbd34d34delivered2017-08-02 18:24:472017-08-02 18:43:152017-08-04 17:35:432017-08-07 18:30:012017-08-15 00:00:00187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-08 18:37:3129.997.789350mauaSPef0996a1a279c26e7ecbd737be23d2352290sao pauloSPutilidades_domesticas40.0268.04.0500.019.08.013.01credit_card137.77housewares-23.543295-46.630743-23.680114-46.452454dc90f19c2806f1abba9e72ad3c3500735NaNSó achei ela pequena pra seis xícaras ,mais é um bom produto\r\n2017-08-08 00:00:002017-08-08 23:26:23
5bfc39df4f36c3693ff3b63fcbea9e90a53904ddbea91e1e92b2b3f1d09a7af86delivered2017-10-23 23:26:462017-10-25 02:14:112017-10-27 16:48:462017-11-07 18:04:592017-11-13 00:00:00187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-31 02:14:1129.9914.109350mauaSPe781fdcc107d13d865fc7698711cc57288032florianopolisSCutilidades_domesticas40.0268.04.0500.019.08.013.01boleto144.09housewares-27.536913-48.509018-23.680114-46.4524541bafb430e498b939f258b9c9dbdff9b13NaNNaN2017-11-08 00:00:002017-11-10 19:52:38
65f49f31e537f8f1a496454b48edbe34da7260a6ccba78544ccfaf43f920b7240delivered2017-08-24 11:31:282017-08-24 11:45:252017-08-25 14:17:552017-08-28 20:12:202017-09-14 00:00:002be03d93320192443b8fa24c0ca6ead983504c0cb71d7fa48d967e0e4c94d59d92017-08-30 11:45:2546.8067.709350mauaSP7a1de9bde89aedca8c5fbad489c5571c1315sao pauloSPutilidades_domesticas59.0189.03.0775.016.016.013.01credit_card1127.45housewares-23.552593-46.640683-23.680114-46.4524548899ca945efd951c97107b49662892271NaNPrezados que porcaria de atendimento ao cliente não se consegue falar com um atendente, só uma máquina burra e ignorante.\r\nVocês não me enviaram o kit de vedação da cafeteira e o filtro e se só isso n2017-08-29 00:00:002017-08-30 02:26:02
75f49f31e537f8f1a496454b48edbe34da7260a6ccba78544ccfaf43f920b7240delivered2017-08-24 11:31:282017-08-24 11:45:252017-08-25 14:17:552017-08-28 20:12:202017-09-14 00:00:001d4a3b5aa064bf44e74f1e71862bea22a57c764b4a836300be881e2ff86e449f92017-08-30 11:45:259.903.0514021ribeirao pretoSP7a1de9bde89aedca8c5fbad489c5571c1315sao pauloSPutilidades_domesticas59.0236.01.0100.021.09.013.01credit_card1127.45housewares-23.552593-46.640683-21.212279-47.7881528899ca945efd951c97107b49662892271NaNPrezados que porcaria de atendimento ao cliente não se consegue falar com um atendente, só uma máquina burra e ignorante.\r\nVocês não me enviaram o kit de vedação da cafeteira e o filtro e se só isso n2017-08-29 00:00:002017-08-30 02:26:02
81fa40f202d5d233b6491e976c557b82250fd5707c28d0a64dc20d67f937dd9badelivered2017-09-23 22:11:102017-09-23 22:25:112017-09-26 17:27:542017-10-19 21:09:212017-11-13 00:00:0018415b1dae10d2dcb36beec370c6a90cd3504c0cb71d7fa48d967e0e4c94d59d92017-09-27 22:25:1128.9021.159350mauaSP35c6ec4630637b3ec0da6e587f245f8369043manausAMutilidades_domesticas59.0322.05.0600.024.05.018.01credit_card150.05housewares-3.076345-59.936005-23.680114-46.4524544b70092fc12f2328972d5ff1022d87e94NaNNaN2017-10-20 00:00:002017-10-23 04:13:41
941c045db2d1876be9f05cf4a787693b2a286f46d6e54cc0179bbb0ee07b0df5edelivered2017-08-16 14:06:302017-08-16 14:55:202017-08-18 15:41:402017-08-21 15:05:132017-08-29 00:00:0015e18248fc768bdb7fc69fd012068d1093504c0cb71d7fa48d967e0e4c94d59d92017-08-22 14:55:2024.907.789350mauaSP4e4fa2b85379e9db6dc59f873f0a97485640sao pauloSPutilidades_domesticas37.0749.04.0600.016.06.020.01credit_card132.68housewares-23.627053-46.736376-23.680114-46.452454b9e086024ceb0234e5950016497a49285NaNNaN2017-08-22 00:00:002017-08-26 02:31:53

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115029f2d6c25c24dec1cb1f8eeba2203634b557244724368844b9ccf59f216d2bb822delivered2018-08-12 20:58:542018-08-12 21:15:112018-08-13 15:25:002018-08-24 17:38:342018-09-12 00:00:00173dacc49330dc5cc9b1c1983c16200e7ccbd753e6863fe7314dc6c0ca5a074e72018-08-16 21:15:11495.00101.5689046blumenauSC74da9bfeaa1c29dc1e5b3a07a4ec17a258410campina grandePBautomotivo54.0299.01.05450.040.016.030.01credit_card6596.56auto-7.241552-35.882677-26.922717-49.148206d71d9f67138ec03bfc0a7e6ccfd6046c4Muito bomNaN2018-08-25 00:00:002018-08-27 03:05:19
1150303cd99c2644261f4b3bf7f70f67e4b5f2d9ef74a91ad65ba313ff15f78c65159ddelivered2017-10-20 08:49:052017-10-20 09:06:102017-10-23 14:23:552017-11-11 14:04:562017-11-22 00:00:001e058c61cf68327217d4bc8b13d07cd64fa8d335d68129e464e60c1826a683e962017-10-26 09:06:10139.9034.7895660tres coroasRSfd358179adc6171c9c6c51e0d91e6b6870845brasiliaDFautomotivo37.02036.01.0600.017.010.024.01credit_card10174.68auto-15.774459-47.876779-29.517802-50.765754395474ca46cc360bedc6e58ebc585c4a4NaNNaN2017-11-12 00:00:002017-11-13 13:06:51
1150318cca5f9f04056cb2d8abb9b845e89b627573237856d19beef367370dd2285f37delivered2018-05-12 07:21:132018-05-12 07:35:422018-05-14 14:59:002018-05-18 20:44:312018-06-04 00:00:0012307240c5347bb41a709a1ab8e38b5ff2c538755f1ca9540af144f266e70df6c2018-05-17 07:35:12334.9924.9239801castro piresMG077c6658adbb60fcf8437e255c00e90612095taubateSPautomotivo57.0372.04.02150.028.028.038.01credit_card6359.91auto-23.075220-45.538807-17.851690-41.4936101a6fe9277fca69b72022e71be4833f365NaNNaN2018-05-19 00:00:002018-05-20 16:21:35
115032a37e262a26000281d0ade3b4eb1ce1e6d0602b514179e7ed50e801cc02769bacdelivered2018-08-06 23:14:272018-08-06 23:25:192018-08-07 14:16:002018-08-13 19:08:402018-08-22 00:00:001987ae6d6e94a608f9ea32bc82a649470466222e777149751370e7e98fb623b0c2018-08-08 23:25:1987.9017.5520931rio de janeiroRJce05c653348a8e630ebbb1fd1e0968d274080goianiaGOautomotivo60.01469.04.0550.036.08.015.01credit_card10105.45auto-16.689245-49.259284-22.876270-43.212926bf0ab4c039d6b05f116d3b9a97344e095NaNNaN2018-08-14 00:00:002018-08-15 10:33:03
1150338edaa376e19d08bc84ab8845682216b44c1e29ec2ed2feac441cf24b25262ed2delivered2018-04-24 08:37:202018-04-24 17:26:252018-04-25 12:24:002018-05-10 22:36:372018-05-14 00:00:001b0498e44190727b728ae4490f2e9b6a5dda37071807e404c5bb2a1590c66326f2018-04-30 09:30:37199.9919.283282sao pauloSP913a4e0cb7fe555e6cffa875ecd58e2e28455sao jose de ubaRJmalas_acessorios25.0712.04.05250.034.023.040.01credit_card1219.27luggage_accessories-21.358247-41.941457-23.590854-46.5413987bbe7636141510158aa577dcfd017b135NaNNaN2018-05-11 00:00:002018-05-13 00:12:43
115034e9f8c67129c6f0bf411b428141ddfbf5b364fc9218a7e1ddd9eb23124ebb7d86delivered2017-04-11 22:09:312017-04-12 13:10:172017-04-17 10:26:592017-04-30 06:57:432017-05-12 00:00:001ecb624b552f939e58c7881de58ece0850936e1837d0c79253456bbb2ffaaef102017-04-26 13:10:1719.4515.562050sao pauloSP11d76022677f3753f19633e2fd0470c335995sao domingos do prataMGferramentas_jardim57.0314.01.0900.062.03.013.01credit_card770.02garden_tools-19.871369-42.960859-23.516544-46.61056395df327760017d154c5f6364e06d70eb5NaNNaN2017-05-01 00:00:002017-05-03 16:38:56
115035e9f8c67129c6f0bf411b428141ddfbf5b364fc9218a7e1ddd9eb23124ebb7d86delivered2017-04-11 22:09:312017-04-12 13:10:172017-04-17 10:26:592017-04-30 06:57:432017-05-12 00:00:002ecb624b552f939e58c7881de58ece0850936e1837d0c79253456bbb2ffaaef102017-04-26 13:10:1719.4515.562050sao pauloSP11d76022677f3753f19633e2fd0470c335995sao domingos do prataMGferramentas_jardim57.0314.01.0900.062.03.013.01credit_card770.02garden_tools-19.871369-42.960859-23.516544-46.61056395df327760017d154c5f6364e06d70eb5NaNNaN2017-05-01 00:00:002017-05-03 16:38:56
115036815ba48fd172b6a71f09180c118a31ca626da291c796ef2695981deb0bbc9eedprocessing2017-09-15 13:24:292017-09-16 03:06:14NaNNaN2017-10-09 00:00:00141f9d5d764dc0fa4928c9177275e57635aaa890629f83706d8d9bfecd8377c1c2017-09-21 03:06:1448.9017.9225720petropolisRJ2dad8f2b565b14983d2792369429b34f95950nova bresciaRSconstrucao_ferramentas_construcao42.0251.01.0400.016.016.016.01boleto166.82construction_tools_construction-29.213983-52.031601-22.436244-43.1403383f93f4f7f09c312dce86e2fb8a4d44301NaNNaN2017-10-12 00:00:002017-10-14 03:44:41